{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = pd.read_csv('../train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 快速预览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* PassengerId => 乘客ID\n",
    "* Survived => 获救情况（1为获救，0为未获救）\n",
    "* Pclass => 乘客等级(1/2/3等舱位)\n",
    "* Name => 乘客姓名\n",
    "* Sex => 性别\n",
    "* Age => 年龄\n",
    "* SibSp => 堂兄弟/妹个数\n",
    "* Parch => 父母与小孩个数\n",
    "* Ticket => 船票信息\n",
    "* Fare => 票价\n",
    "* Cabin => 客舱\n",
    "* Embarked => 登船港口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#中位数\n",
    "data_train.median()\n",
    "\n",
    "# 填充dataframe中Age字段的数据\n",
    "#inplace代表替换，默认不替换\n",
    "data_train.Age.fillna(28.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>Cabin</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>A24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "5              6         0       3   \n",
       "6              7         0       1   \n",
       "7              8         0       3   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "12            13         0       3   \n",
       "13            14         0       3   \n",
       "14            15         0       3   \n",
       "15            16         1       2   \n",
       "16            17         0       3   \n",
       "17            18         1       2   \n",
       "18            19         0       3   \n",
       "19            20         1       3   \n",
       "20            21         0       2   \n",
       "21            22         1       2   \n",
       "22            23         1       3   \n",
       "23            24         1       1   \n",
       "24            25         0       3   \n",
       "25            26         1       3   \n",
       "26            27         0       3   \n",
       "27            28         0       1   \n",
       "28            29         1       3   \n",
       "29            30         0       3   \n",
       "..           ...       ...     ...   \n",
       "861          862         0       2   \n",
       "862          863         1       1   \n",
       "863          864         0       3   \n",
       "864          865         0       2   \n",
       "865          866         1       2   \n",
       "866          867         1       2   \n",
       "867          868         0       1   \n",
       "868          869         0       3   \n",
       "869          870         1       3   \n",
       "870          871         0       3   \n",
       "871          872         1       1   \n",
       "872          873         0       1   \n",
       "873          874         0       3   \n",
       "874          875         1       2   \n",
       "875          876         1       3   \n",
       "876          877         0       3   \n",
       "877          878         0       3   \n",
       "878          879         0       3   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "881          882         0       3   \n",
       "882          883         0       3   \n",
       "883          884         0       2   \n",
       "884          885         0       3   \n",
       "885          886         0       3   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                     Moran, Mr. James    male  28.0      0   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                       Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                      Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                         Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                                Rice, Master. Eugene    male   2.0      4   \n",
       "17                        Williams, Mr. Charles Eugene    male  28.0      0   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                             Masselmani, Mrs. Fatima  female  28.0      0   \n",
       "20                                Fynney, Mr. Joseph J    male  35.0      0   \n",
       "21                               Beesley, Mr. Lawrence    male  34.0      0   \n",
       "22                         McGowan, Miss. Anna \"Annie\"  female  15.0      0   \n",
       "23                        Sloper, Mr. William Thompson    male  28.0      0   \n",
       "24                       Palsson, Miss. Torborg Danira  female   8.0      3   \n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.0      1   \n",
       "26                             Emir, Mr. Farred Chehab    male  28.0      0   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.0      3   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female  28.0      0   \n",
       "29                                 Todoroff, Mr. Lalio    male  28.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "861                        Giles, Mr. Frederick Edward    male  21.0      1   \n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female  48.0      0   \n",
       "863                  Sage, Miss. Dorothy Edith \"Dolly\"  female  28.0      8   \n",
       "864                             Gill, Mr. John William    male  24.0      0   \n",
       "865                           Bystrom, Mrs. (Karolina)  female  42.0      0   \n",
       "866                       Duran y More, Miss. Asuncion  female  27.0      1   \n",
       "867               Roebling, Mr. Washington Augustus II    male  31.0      0   \n",
       "868                        van Melkebeke, Mr. Philemon    male  28.0      0   \n",
       "869                    Johnson, Master. Harold Theodor    male   4.0      1   \n",
       "870                                  Balkic, Mr. Cerin    male  26.0      0   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.0      1   \n",
       "872                           Carlsson, Mr. Frans Olof    male  33.0      0   \n",
       "873                        Vander Cruyssen, Mr. Victor    male  47.0      0   \n",
       "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.0      1   \n",
       "875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.0      0   \n",
       "876                      Gustafsson, Mr. Alfred Ossian    male  20.0      0   \n",
       "877                               Petroff, Mr. Nedelio    male  19.0      0   \n",
       "878                                 Laleff, Mr. Kristo    male  28.0      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
       "881                                 Markun, Mr. Johann    male  33.0      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "883                      Banfield, Mr. Frederick James    male  28.0      0   \n",
       "884                             Sutehall, Mr. Henry Jr    male  25.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  28.0      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket      Fare        Cabin Embarked  \n",
       "0        0         A/5 21171    7.2500          NaN        S  \n",
       "1        0          PC 17599   71.2833          C85        C  \n",
       "2        0  STON/O2. 3101282    7.9250          NaN        S  \n",
       "3        0            113803   53.1000         C123        S  \n",
       "4        0            373450    8.0500          NaN        S  \n",
       "5        0            330877    8.4583          NaN        Q  \n",
       "6        0             17463   51.8625          E46        S  \n",
       "7        1            349909   21.0750          NaN        S  \n",
       "8        2            347742   11.1333          NaN        S  \n",
       "9        0            237736   30.0708          NaN        C  \n",
       "10       1           PP 9549   16.7000           G6        S  \n",
       "11       0            113783   26.5500         C103        S  \n",
       "12       0         A/5. 2151    8.0500          NaN        S  \n",
       "13       5            347082   31.2750          NaN        S  \n",
       "14       0            350406    7.8542          NaN        S  \n",
       "15       0            248706   16.0000          NaN        S  \n",
       "16       1            382652   29.1250          NaN        Q  \n",
       "17       0            244373   13.0000          NaN        S  \n",
       "18       0            345763   18.0000          NaN        S  \n",
       "19       0              2649    7.2250          NaN        C  \n",
       "20       0            239865   26.0000          NaN        S  \n",
       "21       0            248698   13.0000          D56        S  \n",
       "22       0            330923    8.0292          NaN        Q  \n",
       "23       0            113788   35.5000           A6        S  \n",
       "24       1            349909   21.0750          NaN        S  \n",
       "25       5            347077   31.3875          NaN        S  \n",
       "26       0              2631    7.2250          NaN        C  \n",
       "27       2             19950  263.0000  C23 C25 C27        S  \n",
       "28       0            330959    7.8792          NaN        Q  \n",
       "29       0            349216    7.8958          NaN        S  \n",
       "..     ...               ...       ...          ...      ...  \n",
       "861      0             28134   11.5000          NaN        S  \n",
       "862      0             17466   25.9292          D17        S  \n",
       "863      2          CA. 2343   69.5500          NaN        S  \n",
       "864      0            233866   13.0000          NaN        S  \n",
       "865      0            236852   13.0000          NaN        S  \n",
       "866      0     SC/PARIS 2149   13.8583          NaN        C  \n",
       "867      0          PC 17590   50.4958          A24        S  \n",
       "868      0            345777    9.5000          NaN        S  \n",
       "869      1            347742   11.1333          NaN        S  \n",
       "870      0            349248    7.8958          NaN        S  \n",
       "871      1             11751   52.5542          D35        S  \n",
       "872      0               695    5.0000  B51 B53 B55        S  \n",
       "873      0            345765    9.0000          NaN        S  \n",
       "874      0         P/PP 3381   24.0000          NaN        C  \n",
       "875      0              2667    7.2250          NaN        C  \n",
       "876      0              7534    9.8458          NaN        S  \n",
       "877      0            349212    7.8958          NaN        S  \n",
       "878      0            349217    7.8958          NaN        S  \n",
       "879      1             11767   83.1583          C50        C  \n",
       "880      1            230433   26.0000          NaN        S  \n",
       "881      0            349257    7.8958          NaN        S  \n",
       "882      0              7552   10.5167          NaN        S  \n",
       "883      0  C.A./SOTON 34068   10.5000          NaN        S  \n",
       "884      0   SOTON/OQ 392076    7.0500          NaN        S  \n",
       "885      5            382652   29.1250          NaN        Q  \n",
       "886      0            211536   13.0000          NaN        S  \n",
       "887      0            112053   30.0000          B42        S  \n",
       "888      2        W./C. 6607   23.4500          NaN        S  \n",
       "889      0            111369   30.0000         C148        C  \n",
       "890      0            370376    7.7500          NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 尝试从性别进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按列做汇总统计\n",
    "data_train.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "female    233\n",
       "male      109\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生还者人数\n",
    "survived= data_train[data_train.Survived==1].Sex.value_counts()\n",
    "survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      468\n",
       "female     81\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 未生还者人数\n",
    "death=data_train[data_train.Survived==0].Sex.value_counts()\n",
    "death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>233</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>death</th>\n",
       "      <td>81</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          female  male\n",
       "survived     233   109\n",
       "death         81   468"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([survived,death],index=['survived','death'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11149d860>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar() # 与df.plot(kind='bar')等价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>233</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>109</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived  death\n",
       "female       233     81\n",
       "male         109    468"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 转置\n",
    "df =df.T\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11157d748>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>233</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>109</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived  death\n",
       "female       233     81\n",
       "male         109    468"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['p_survived']=df.survived/(df.survived+df.death)\n",
    "df['p_death']=df.death/(df.survived+df.death)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>death</th>\n",
       "      <th>p_survived</th>\n",
       "      <th>p_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>233</td>\n",
       "      <td>81</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>0.257962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>109</td>\n",
       "      <td>468</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>0.811092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        survived  death  p_survived   p_death\n",
       "female       233     81    0.742038  0.257962\n",
       "male         109    468    0.188908  0.811092"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111680c18>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['p_survived','p_death']].plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "性别对生还的影响挺大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从年龄进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.00    202\n",
       "24.00     30\n",
       "22.00     27\n",
       "18.00     26\n",
       "19.00     25\n",
       "30.00     25\n",
       "21.00     24\n",
       "25.00     23\n",
       "36.00     22\n",
       "29.00     20\n",
       "32.00     18\n",
       "27.00     18\n",
       "35.00     18\n",
       "26.00     18\n",
       "16.00     17\n",
       "31.00     17\n",
       "34.00     15\n",
       "20.00     15\n",
       "23.00     15\n",
       "33.00     15\n",
       "39.00     14\n",
       "17.00     13\n",
       "42.00     13\n",
       "40.00     13\n",
       "45.00     12\n",
       "38.00     11\n",
       "50.00     10\n",
       "2.00      10\n",
       "4.00      10\n",
       "47.00      9\n",
       "        ... \n",
       "71.00      2\n",
       "59.00      2\n",
       "63.00      2\n",
       "0.83       2\n",
       "30.50      2\n",
       "70.00      2\n",
       "57.00      2\n",
       "0.75       2\n",
       "13.00      2\n",
       "10.00      2\n",
       "64.00      2\n",
       "40.50      2\n",
       "32.50      2\n",
       "45.50      2\n",
       "20.50      1\n",
       "24.50      1\n",
       "0.67       1\n",
       "14.50      1\n",
       "0.92       1\n",
       "74.00      1\n",
       "34.50      1\n",
       "80.00      1\n",
       "12.00      1\n",
       "36.50      1\n",
       "53.00      1\n",
       "55.50      1\n",
       "70.50      1\n",
       "66.00      1\n",
       "23.50      1\n",
       "0.42       1\n",
       "Name: Age, Length: 88, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "survived=data_train[data_train.Survived==1].Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "death=data_train[data_train.Survived==0].Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame([survived,death],index=['survived','death'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>881</th>\n",
       "      <th>882</th>\n",
       "      <th>883</th>\n",
       "      <th>884</th>\n",
       "      <th>885</th>\n",
       "      <th>886</th>\n",
       "      <th>887</th>\n",
       "      <th>888</th>\n",
       "      <th>889</th>\n",
       "      <th>890</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>death</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 891 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0     1     2     3     4     5     6    7     8     9    ...   \\\n",
       "survived   NaN  38.0  26.0  35.0   NaN   NaN   NaN  NaN  27.0  14.0  ...    \n",
       "death     22.0   NaN   NaN   NaN  35.0  28.0  54.0  2.0   NaN   NaN  ...    \n",
       "\n",
       "           881   882   883   884   885   886   887   888   889   890  \n",
       "survived   NaN   NaN   NaN   NaN   NaN   NaN  19.0   NaN  26.0   NaN  \n",
       "death     33.0  22.0  28.0  25.0  39.0  27.0   NaN  28.0   NaN  32.0  \n",
       "\n",
       "[2 rows x 891 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>8</th>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>9</th>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
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       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
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       "      <th>15</th>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>17</th>\n",
       "      <td>28.0</td>\n",
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       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
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       "      <th>25</th>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>26</th>\n",
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       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
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       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
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       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
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       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
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       "      <th>873</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  death\n",
       "0         NaN   22.0\n",
       "1        38.0    NaN\n",
       "2        26.0    NaN\n",
       "3        35.0    NaN\n",
       "4         NaN   35.0\n",
       "5         NaN   28.0\n",
       "6         NaN   54.0\n",
       "7         NaN    2.0\n",
       "8        27.0    NaN\n",
       "9        14.0    NaN\n",
       "10        4.0    NaN\n",
       "11       58.0    NaN\n",
       "12        NaN   20.0\n",
       "13        NaN   39.0\n",
       "14        NaN   14.0\n",
       "15       55.0    NaN\n",
       "16        NaN    2.0\n",
       "17       28.0    NaN\n",
       "18        NaN   31.0\n",
       "19       28.0    NaN\n",
       "20        NaN   35.0\n",
       "21       34.0    NaN\n",
       "22       15.0    NaN\n",
       "23       28.0    NaN\n",
       "24        NaN    8.0\n",
       "25       38.0    NaN\n",
       "26        NaN   28.0\n",
       "27        NaN   19.0\n",
       "28       28.0    NaN\n",
       "29        NaN   28.0\n",
       "..        ...    ...\n",
       "861       NaN   21.0\n",
       "862      48.0    NaN\n",
       "863       NaN   28.0\n",
       "864       NaN   24.0\n",
       "865      42.0    NaN\n",
       "866      27.0    NaN\n",
       "867       NaN   31.0\n",
       "868       NaN   28.0\n",
       "869       4.0    NaN\n",
       "870       NaN   26.0\n",
       "871      47.0    NaN\n",
       "872       NaN   33.0\n",
       "873       NaN   47.0\n",
       "874      28.0    NaN\n",
       "875      15.0    NaN\n",
       "876       NaN   20.0\n",
       "877       NaN   19.0\n",
       "878       NaN   28.0\n",
       "879      56.0    NaN\n",
       "880      25.0    NaN\n",
       "881       NaN   33.0\n",
       "882       NaN   22.0\n",
       "883       NaN   28.0\n",
       "884       NaN   25.0\n",
       "885       NaN   39.0\n",
       "886       NaN   27.0\n",
       "887      19.0    NaN\n",
       "888       NaN   28.0\n",
       "889      26.0    NaN\n",
       "890       NaN   32.0\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.T\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111a7afd0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111b78f60>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直方图分割\n",
    "df.plot.hist(stacked=True,bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111d8fc18>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 密度图\n",
    "df.plot.kde()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      29.361582\n",
       "std       13.019697\n",
       "min        0.420000\n",
       "25%       22.000000\n",
       "50%       28.000000\n",
       "75%       35.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看年龄的分布，来取横轴的取值范围\n",
    "data_train.Age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c27ebe0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ilDofTRyqZaWvhICuEN63yR9ZmXqcB/6dQmyoH4seHEOvLgFnzsWE+DG+dxi3JsXwyHubuemfa1n04BjiwwPqX8jLH8ISdGSVUg6mneOq5dhsVv9G/GSwlhxr1NbMUzwwL4XeXQP48KGx5ySN2sb1CuOjh8diDNz91kaOF5U3fMGIRB1ZpZSDaeJQLef4bijNa/JquHnFFTw8P4Uugd7Mu38UwX6eFyzfq0sAb983grziCh5/f+s5/SBnRCRanePFxy/mK1BKNYEmDtVyzmwT23jiMMbwzEfbOVFSyWuzhxPi79WkWwyO7sQL1w/kh/R8Xl2RVr/A6Q7ynO1NDFop1VyaOFTLSV8Bob0hOLrRoku25/DtnuM8eUUfBkQGN+s2twyP5rrESP6+fD/7jhWde7LbIOtZm6uUchhNHKplVFfAobXQc2qjRfOLK5izeBeJMZ340fgezb6ViDDnuv4EeHvw6493YKvdZOUTDJ17aAe5Ug6kiUO1jMProboMek5ptOiflqVSVF7FH28ejLtb0zrR6woN8ObZq/uRknGSJduPnHsyIlETh1IOpIlDtYwDy8HNw5oxfgH7jhWxKDmT2aPj6NMt8JJuefOwaPpFBPGnZalUVNecPRGRCKcyrGXWlVItThOHahnpK6xl1L0vnAxe+nIv/t4ePDa11yXf0s1NePbqvmSdLGPeDxlnT0QMtp6P7rjkeyil6tPEoS5dSZ7VNBR/4Waq9en5fLf3OI9M7kXnJo6iasyE3uFMTAjn78vTKCqvsg52Oz2ySpurlHIETRzq0p0ehttIx/jL3+yja5A3942La9HbP3lFAgVlVcxbb691BIRDUJQOyVXKQTRxqEt3YAX4dILIIectsvHgCTYcPMFDk3ri49mElW6bYXB0JyYmhPPm6oOUVdr7OroN1hqHUg6iiUNdGmOsjvH4SeB2/oTwjxVphAV4cduI7g4J49EpvcgvqeT9jfY9xyMSIW8fVJY45H5KdWSaONSlydsHRUcu2L+xLfMU3+/L5ccT4pu2r8ZFGNkjhJFxIbz+fTqV1Tb7DHIDR3c65H5KdWSaONSlObDcer7A/I3Xv08nyMeDu0bHOjSUhybHc7SwnC935tQaWaX9HEq1NE0c6tLs/wZCe0HnuAZPZ50s5cudOdw+qjsB3o5dxX9yQhd6hPnz9tpDVue4X6guPaKUA2jiUBevohgOrYaE6ect8u66Q4gI94yJc3g4bm7CPWNi2Zp5ii2Zp3QGuVIOoolDXbyDq6CmEnpf0eDp4opqFmzM5OpBEUR28m2VkG4eHk2AtwfvrDtkjaw6vtdaR0sp1WI0caiLt+8r8A6C7mMaPP1hciZFFdX8qIXnbVxIoI8ntyRF88X2HE516g+2Kji+p9Xur1RHoIlDXRxjYN/X1qQ/j/qzwGtshrfXHWJY904M7d65VUO7Z0wcNcbw8ZFQ64A2VynVojRxqIuTsw2Kj0LClQ2e/n5fLhn5pdw3rvnLpl+quDB/JieE89oOG8Y7UBOHUi1ME4e6OPuWAQK9Lm/w9HsbMggL8Gb6wG6tG5fdHaNiOVZcxcnAvjokV6kWpolDXZz9yyA6yVoXqo4jp8pYvvc4s0ZE4+nunB+xKX3C6RrkzaaKGGsSYE21U+JQqj3SxKGar+goZKdA74abqRZsysSAw5YXaQoPdzduTYph2Ymu1gZT+fudFotS7Y0mDtV8e5ZYz/2uq3equsbGwk2HmZQQTkyIXysHdq5bk2LYZeKsN7pSrlItRhOHar49SyC0N4T3qXfqu73HOVZYwR0jnVfbOC0mxI+InolU4IXtiM4gV6qlaOJQzVN6Ag6tgf4zQOrvF/6fDYfpFuTD1L5dnBBcfbeNimO3rTsF6cnODkWpdsOhiUNEpotIqoikicgzDZz3FpGF9vMbRCTOfnykiGy1P7aJyI2OjFM1Q+pSMDUNNlNlnijl+/25zBoRg4eTOsXrmtavKwfc4/HO2wk2m7PDUapdcNhvt4i4A68CVwH9gdtFpH+dYvcDJ40xvYCXgZfsx3cCScaYIcB04DURcewKeapp9iyB4BiIqL9p0/sbDyPAbSNjWj+u8/B0dyMgbjh+ppTczH3ODkepdsGR/y0cCaQZY9KNMZXAAuD6OmWuB961v/4QmCYiYowpNcacHj/pAxgHxqmaqqLIWka933X1mqkqq20sSs5kat+uRAS3zrpUTTV4xCQAUjascHIkSrUPjkwcUUBmrfdZ9mMNlrEnigIgFEBERonILmAH8FCtRKKcZd8ya1HDfjPqnfpm9zHyiiu5c7TzO8XrikwYRjXu5O3fhM2m/wdR6lK5RkN0A4wxG4wxA4ARwK9FxKduGRF5QESSRSQ5Nze39YPsaHZ9AgHdIGZkvVPvbcggqpMvE3vXnxDodB7eFAf1IqZ8P+sP5js7GqXaPEcmjmygdmN3tP1Yg2XsfRjBwDm/2caYPUAxMLDuDYwxrxtjkowxSeHhLvgHqz0pOwn7v4aBN9fbW/xgXgnrDuRzx6juuLvVH2nlCgLihjPQ/RCLTu9JrpS6aI5MHJuA3iLSQ0S8gNuAxXXKLAbusb+eCSw3xhj7ZzwARCQW6AsccmCsqjF7lljNVINm1jv1/sbDeLgJtyRFOyGwpvGIGkoohWzetYuC0ipnh6NUm+awxGHvk3gUWAbsARYZY3aJyAsicrqR/E0gVETSgCeA00N2xwPbRGQr8AnwiDEmz1GxqibY8QGE9ITIoeccLq+q4YPkTK4Y0JUugfVaE11H1DAA+tvS+Gxb3YqvUqo5mjTEVUQ+xvoj/6UxpsmD4Y0xS4GldY79ttbrcuCWBj43D5jX1PsoByvMgYOrYdLT9UZTLdt1lJOlVdw5KtZJwTVRt0Hg7sVlQYd5a2Mmd7fCVrZKtVdNrXH8H3AHsF9EXhSR+mtNqPZr18eAabCZ6r31h4kL9WNMfGjrx9UcHt7QbRATfDPYnVPIzuwCZ0ekVJvVpMRhjPnWGHMnMAyrr+FbEVknIveJiKcjA1QuYMeHEJEIYb3POZx6tIiNh05wx6juuLlop/g5opLoUrwHPw/Dwk2ZjZdXSjWoyX0cIhIK3Av8GNgC/A0rkXzjkMiUa8g/AEc2w6B6LYr8Z0MGXh5uzBzuOjPFLyh6BFJVyn29y/h0azblVTXOjkipNqlJiUNEPgFWA37AdcaYGcaYhcaYx4AARwaonGz7IkBgwE3nHC6pqObjzdlcMyiCEP/6e467pOjhANzU5ShF5dV8uTPHyQEp1TY1tcbxL2NMf2PM/xhjcsBaoBDAGJPksOiUc9lssPU/ED8Zgs+d9L9k2xGKKqq5ywVnip9X5x7gF0p8xR5iQ/1YsFGbq5S6GE1NHP/dwLEfWjIQ5YIOroSCwzBs9jmHjTHM35BB326BDOve2TmxXQwRiBqOZKdwa1IMGw6e4GBeibOjUqrNuWDiEJFuIjIc8BWRoSIyzP6YjNVspdqzzfPAtzP0vfacw9uyCtiZXcido2ORBvbkcGnRIyA3lVsGBuMmsChZax1KNVdj8ziuxOoQjwb+Wut4EfCsg2JSrqD0BOz9HJJ+ZA1lreW99Rn4eblzw5BIJwV3CaKGA4YuRbuY2rcLH6Vk8cvLE1xm/xCl2oIL/rYYY941xkwB7jXGTKn1mGGM+biVYlTOsH2htcTI0HObqQpKq1iy/Qg3DI0i0KcNjsSOsjrIydrErUkxHC+qYGWqLpCpVHNcsMYhIncZY+YDcSLyRN3zxpi/NvAx1dYZYzVTRQ6DbueuLfnR5izKq2zcOaoNdYrX5tsJwvpA5kamjPsl4YHeLNiUyWX9uzo7MqXajMbq5/725wAgsIGHao+ObIbjuxrsFH9vQwZDu3diQGSwk4JrAbFj4fB6PMVw87BoVqQe53hhubOjUqrNuGCNwxjzmv35+dYJR7mEzfPAw9daQr2WH9LzOZBbwp9vSXRSYC0kdhykvA3HdjJrRC/mrjrAh5uzeGRyL2dHplSb0NQJgH8UkSAR8RSR70QkV0TucnRwygkqS2HnRzDgBvA5t1bx73UZdPLz5NrBEU4KroXEjrGeM9bRI8yfkT1CWLQpE2N0d0ClmqKpQ0muMMYUAtdirVXVC3jKUUEpJ9r9GVQU1usUzzxRyte7j3LbiO74eLqf58NtRHA0dOoOGesAuG1EDIfyS9lw8ISTA1OqbWhq4jjdpHUN8IExRpcWba82/9vadyN27DmH563PQES4e4yLL5/eVLHjrMRhDFcNjCDQ24NFuvChUk3S1MTxuYjsBYYD34lIOKC9ie1NXhocXgdD7zpn343SymoWbDzM9AHdiOzk68QAW1DsWCjNg7z9+Hq5c/3QSJbuzKGgTHcHVKoxTV1W/RlgLJBkjKkCSoDrHRmYcoIt80DcYcgd5xz+aHM2heXV3DcuzjlxOUJ3e40qYy0As5K6U15lY/G2I04MSqm2oTnTZfsCs0Tkbqz9wa9wTEjKKWqqYdv7kHAlBHY7c9hmM7yz9iCDooIZHtuG1qVqTGhP8O8Ch60l1wZGBdE/IoiFmw47OTClXF9TR1XNA/6MtRf4CPtDV8VtT/Z/DcXH6nWKr07L40BuCfeNi2t761JdiIjVXGXvIBcRbh8Zw87sQrZlnnJycEq5tqbWOJKAccaYR4wxj9kfP3NkYKqVbZkHAV2h97kVybfXHiQswJtr2voQ3IbEjYeCTDiRDsCNw6IJ8Pbg3XWHnBuXUi6uqYljJ9Ct0VKqbSo6CvuWQeLt4H52Tmjq0SJWpuYye3Qs3h5tfAhuQ+KnWM8HVgAQ4O3BzOHRfL49h7ziCicGppRra2riCAN2i8gyEVl8+uHIwFQr2vofMDX1mqle+/4Avp7u7WcIbl2hPSG4OxxYfubQ7DGxVNbYeH+D9nUodT6NLat+2hxHBqGcyBjYMt+a1xB2dsmNI6fKWLz1CHeNjqVzW9katrlEoOdk2PWpNTjA3YOe4QFM6B3G/A0ZPDS5J5663LpS9TR1OO4qrBnjnvbXm4DNDoxLtZaMdXDiQL3axltrDmKAH0/o4Zy4WkvPqdZM+SNnf5zvHRvHscIKlu066sTAlHJdTR1V9RPgQ+A1+6Eo4FNHBaVa0ZZ54B0E/c9OyykoreL9jYe5bnAE0Z3b+UaPPSYBck5z1eQ+Xege4qed5EqdR1Pr4T8FxgGFAMaY/UAXRwWlWkl5gdVMM/Bm8DqbIOZvyKCksoYHJvZ0YnCtxC8EIodC2ndnDrm7WUurbDp0kp3ZurqOUnU1NXFUGGMqT78REQ9AlxJt63Z+DNVl5zRTlVfV8PbaQ0xKCKd/ZJATg2tFvS+H7GQoyTtz6JakGPy83HljdboTA1PKNTU1cawSkWcBXxG5HPgAWOK4sFSr2DIfuvSHqGFnDi1KziSvuIKHJnWA2sZpfa4CY7OGJNsF+3py+8juLNmeQ9bJUicGp5TraWrieAbIBXYADwJLgeccFZRqBcf3Wv/LrrWgYUV1Df9ceYCRcSGMjg9xcoCtKGIIBEVB6tJzDv9ofA8EeHPNQefEpZSLauqoKhtWZ/gjxpiZxph/Gd31pm3bOh/cPGDwrDOHPkjOIqegnJ9N692+lhdpjIhV6ziwHKrKzhyO6uTLjMRIFm7K5FRp5QUuoFTHcsHEIZY5IpIHpAKp9t3/fts64SmHqKmCbQsgYTr4hwFQWW3jnysPMKx7J8b1CnVygE7Q5yqoKoX0VeccfmBSPKWVNcxfn+GkwJRyPY3VOH6BNZpqhDEmxBgTAowCxonILxq7uIhMF5FUEUkTkWcaOO8tIgvt5zeISJz9+OUikiIiO+zPU5v9lanz2/81lOSe0yn+8eYssk+VdbzaxmlxE8ArEFK/OOdw325BTO4TzjvrDlFeVeOk4JRyLY0ljtnA7caYM428xph04C7g7gt9UETcgVeBq4D+wO0i0r9OsfuBk8aYXsDLwEv243nAdcaYQcA9wLymfTmqSbbMtxY07HUZAFU1Nl5dmUZidDCTEsKdHJyTeHhDn+mwZwlUn9ss9eDEnuQVV/JhSpaTglPKtTSWODyNMXl1DxpjcgHPRj47EkgzxqTbh/IuoP7mT9cD79pffwhMExExxmwxxpzeUWcX1mgu70bup5roYBXMAAAgAElEQVSi6Fi9BQ0/2ZJN5okyHr+sg9Y2Ths4E8pOQvqKcw6Pjg8hMaYTc1cdoKrG5qTglHIdjSWOC/UINtZbGAXU3sQ5y36swTLGmGqgAKjbwH4zsNkYo8uVtoTtC+wLGt4FWCOp/vbtfgZHBzOlTwef09lzKvh2hh0fnHNYRPj5tN5knSzj481a61CqscSRKCKFDTyKgEGODk5EBmA1Xz14nvMPiEiyiCTn5uY6Opy2zxjY8h7EjIKw3gC8v+Ew2afKeOrKPh27tgHg4QX9b4C9S6Gy5JxTk/uEkxgdzN+Xp2mtQ3V4F0wcxhh3Y0xQA49AY0xjTVXZQEyt99H2Yw2Wsc9GDwby7e+jgU+Au40xB84T3+vGmCRjTFJ4eAdtm2+OrGTISz1T2yipqOYfK9IYEx/K+F5hTg7ORQyaCVUlkPrlOYdFhJ9flqC1DqVo3p7jzbUJ6C0iPUTEC7gNqLuHx2Kszm+w9jFfbowxItIJ+AJ4xhiz1oExdixb5oGnHwy4EbB298srruSp6VrbOKP7WAiOsQYQ1DG5TziJMZ201qE6PIclDnufxaPAMmAPsMgYs0tEXhCRGfZibwKhIpIGPIE1Qx3753oBvxWRrfZHB2+Av0SVJdbaVANuBO9ATpVW8tr36VzWryvDund2dnSuw80Nht1tdZCfOHedKqvWYfV1LErOPM8FlGr/HLpLjTFmqTEmwRjT0xjze/ux3xpjFttflxtjbjHG9DLGjLQP9cUY89/GGH9jzJBaj+OOjLXd270YKotgyJ0AzF2VTnFFNU9emeDkwFzQ0Nkg7pDybr1TkxPCGRHXmVe+3U9pZbUTglPK+XR7s45iy3wIiYfYsRwvLOeddQe5PjGSvt06yAq4zREUYc0k3zK/3pwOEeGZq/qSW1TBG6t1DSvVMWni6AhOpEPGGqu2IcIr3+2nusbwi8u1tnFew++D0jzY/Vn9U7EhXDmgK6+tOkB+sY4SVx2PJo6OYOt/QNwg8Xb2HStiwcbD3DU6lthQf2dH5rp6ToWwBFj7N2sYcx2/mt6X8mobf1+e5oTglHIuTRztna3GShw9p0FwFH9Yugd/bw9+Nq23syNzbW5uMO5xOLYDDnxX73TP8ABmjYhh/voM0o4XOyFApZxHE0d7l74CCrNh6J2s3p/LytRcHpvaixB/L2dH5voG3QqBkbDmlQZPP3F5Ar5e7jy/ZBe6y4DqSDRxtHdb5oNvZ2p6X8Xvv9hDTIgv94yNc3ZUbYOHF4z5KRxaDRk/1DsdFuDNLy5LYPX+PL7ZfcwJASrlHJo42rPSE7D3Cxg8iw+3HWfv0SKent4Xbw93Z0fWdiTdBwHd4JvfNtjXMXtMLAldA/ivL3brsuuqw9DE0Z7t+ABqKikbcDt//nofw7p34ppBEc6Oqm3x8ocpv4asjbD383qnPd3dmHPdADJPlPHaqvQGLqBU+6OJoz3bMg+6DeafqX7kFlXwm2v669IiF2PIXRDWB76dU29eB8DYXmFcOziCV1ekaUe56hA0cbRXOdvg6A4K+t3G698f4JrBEQyP1aVFLoq7B1z5e8hPg7UNd5T/7roB+Hq58+uPt2OzaUe5at80cbRXW+aDuze/zxyEzcAz0/s6O6K2rfflMOAm+P5PkLuv3unwQG+eu6Yfmw6d5L2Nh50QoFKtRxNHe1RVDtsXkRdzBYt2FvHQpJ7EhPg5O6q276qXwNMXFj9mzY+pY+bwaMb3CuOlL/dy5FSZEwJUqnVo4miP9n4O5af4S+5Iojr58vCkns6OqH0I6AJX/Qky18OqP9Y7LSL84cZB2IzhyQ+2aZOVarc0cbRHW+ZT7BPJgvwePHdNP3y9dPhti0mcBYl3wKqX4OD39U53D/Xjt9f2Z92BfN5co4sgqvZJE0d7c+owJn0l/y4fx7heXZg+sJuzI2p/rv6TtfXuB/fCifrJYdaIGK7o35U/LUtl95HC1o9PKQfTxNHebP0PBlhQOYE5M3T4rUN4B8DtC8DY4L1boOzkOadFhBdvHkywnyePL9ii+3aodkcTR3tis1GZPI91tgFcMXYEvboEOjui9iu0J8x6D04espJH+bk1ixB/L/56ayJpucX85pOdupaValc0cbQjNemr8CrOYqnH5Tx+ma5+63Bx4+CWtyF7s5U8KorOOT2hdzi/uCyBT7ZkM399hpOCVKrlaeJoRw5/+xoFxo9RV88m0MfT2eF0DP2ug5lvQdYmePtqKMw55/SjU3oxpU84L3y+m82HT57nIkq1LZo42onjx44QmfMtGwMvY8bweGeH07EMuAHuWAj5B+CNy+DY7jOn3NyEl2cNoVuwDw/OSyFb53eodkATRzuxetEreEsVA2b8XDvEnaH35XDfUrBVwZuXw44Pz5zq5OfFm/eMoLyqhh+9vYnC8ionBqrUpdPE0Q58uyuHYbmfciRoCJEJw50dTscVOQR+sgK6DoCP7ofPfwGVpQAkdA1k7l3DOZBbzE/f20xVjc3JwSp18TRxtHElFdUs+fQ/9HA7RpepDzs7HBUcBfd+YW07m/wWzB0Hh9YCMK5XGH+4aRCr9+fxy0XbqNGZ5aqN0sTRxv31m31ML/+SKu8QPAbe6OxwFIC7J1z+AtyzxJrr8c7V8MUvobyAW5Ni+NX0PizedoRnPtKVdFXbpImjDUs+dIIv1qZwhftmPJNmg4e3s0NStfWYCA+vg9E/hU1vwv8Og+S3eWRiD342rTcfpGTxu8W7NHmoNkcTRxtVWlnNkx9s48f+a3CnBobf6+yQVEO8/GH6H+CBlRCWAJ//HF6byC/ij/DgxHjmrc/gyQ+3aZ+HalM0cbRRL325l6z8QmZ7rYSe0yBEh+C6tMgh1qirW96FikJk3vU8c/wp/jSilI83Z/PQvBTds1y1GZo42qB1aXm8+0MGL/Y9iHfpURj5E2eHpJpCxJrz8dNNMP1FJDeVW3b8mHVRf+fkvjXc9vp6jhWWOztKpRol7WUNnaSkJJOcnOzsMByuqLyK6a+sxstd+C7oedwqi6w/RG76f4A2p7IUkt+ENS9DaT6rzRDe9riVn959O8NjQ5wdneogRCTFGJPUnM/oX5s2xBjDMx/v4GhhOf+cVIVbzhYY/bAmjbbKyw/GPgaPb4dpv2OM72HeqnmW8jev4/PFi7TTXLks/YvThry/MZMvtufwxOUJ9D34b/DtDIm3Ozssdam8A2DCE3g8sZOyyc8z0DObazf/hNQXJ5C3/WtoJ60Cqv1waOIQkekikioiaSLyTAPnvUVkof38BhGJsx8PFZEVIlIsIv9wZIxtxd6jhTy/ZBcTeofx8CA32PM5DL/PGrWj2gcvf3wn/5ygp3ezpf8zhFRkEfbxLRx7ZSLVqcs0gSiX4bDEISLuwKvAVUB/4HYR6V+n2P3ASWNML+Bl4CX78XLg/wFPOiq+tqSgrIpH3ttMoI8nf711CG7r/wFuHtop3k6Jlx9Db/01FY9s5t2Qn1F16gge799K4d8nYPZ+oQlEOZ0jaxwjgTRjTLoxphJYAFxfp8z1wLv21x8C00REjDElxpg1WAmkQ6uxGX72/hYO55fyjzuGEm7Lgy3zYOhdEBTp7PCUA3XvGsLdj71A6syVvOT1CCfzjiEL7qD4f8dgdn0KNp37oZzDkYkjCsis9T7LfqzBMsaYaqAACHVgTG3O/yzdw6p9ubxw/UBGx4fC2r9Zy1iM/4WzQ1OtQESYNiiGn//qv1kz/Ste8HiMY/mnkA/u4dRfkyjfsghsOv9Dta423TkuIg+ISLKIJOfm5jo7nBb3/sbDvLHmIPeOjeOOUd2h6CikvAOJt0HnWGeHp1qRt4c7d47tydPPPE/KtV/ykv9THC8sx+ezn5D70hAOfPMGtmpdrl21Dkcmjmwgptb7aPuxBsuIiAcQDOQ39QbGmNeNMUnGmKTw8PBLDNe1fLE9h2c/2cHkPuE8d00/6+Da/wVbNUz4pXODU07j7eHOrSN78Ksnf0PRj77n7cg55JdDz7W/JPO/B7Ng3mus259LZbU2YynH8XDgtTcBvUWkB1aCuA24o06ZxcA9wA/ATGC5aYUZiadKK9l8+CQHjpdwtLCc6hobbm5CtyAfYkL8GBLTiYhgH6dtiPTdnmP8fOEWkmI78887h+Ph7gYFWdZkscG36vIiChFheFwYwx/4BSXlP2XjigXEbP4Ttx34Fev2vcOdbvcQnjCCqX27MrlPOGEBugCmajkOnTkuIlcDrwDuwFvGmN+LyAtAsjFmsYj4APOAocAJ4DZjTLr9s4eAIMALOAVcYYzZ3cBtgMZnjpdX1fDplmw+3pxNcsYJTs+t8vNyx8vDjapqGyWVZ9uKozr5cnn/rlw5oBsj4jpbf7xbwWdbs/nlom30iwhi/o9HEexr3zv8k4dh54fwaLI2U6mG1VRRseEtZOX/4FF5iqVuk/mv0ps5LiEkRnfisn5dmNavK327BeoukeqMi5k53u6XHKmstvHOuoPMXZXOiZJKEroGcOWAbozvFUZC10A6+3udKVtUXsXBvBK2Zp5i9f48vt+XS0W1jW5BPtw2MoZZI2KICPZ1SPzGGP61Op3/+XIvI+NCeOOeJAJ97Enj6A6YOwHGPgpX/LdD7q/akbJTsPovmA1zMeLOhsi7+WvJFWzKrgCs/xRNsyeR0fEheHu4Ozlg5UyaOOokjnUH8vjNJzs5mFfCxIRwHp7Uk9HxIU3+31ZpZTUr9uayKDmT7/fnIsC0fl2ZPTqW8b3CcHNrmf+1FVdU8/RH2/liew5XDezGy7OG4ONp/2U2Bv49A3K2w+NbrdniSjXFyUPwzW9h92cQHEPB+Of40jaGb/fmsiYtl/IqGwHeHkwf2I0bhkQxpmco7i30M63aDk0c9sRRUV3DX77ex79WpxMX6s9vr+vPlD5dLun6mSdK+c/GwyzalEl+SSVxoX7cNTqWmcOj6eTn1fgFzmP53mP8v093kVNQxlNX9uWhSfHnJrbtH8DHP4ar/6wT/tTFObQGvnwGju2A7mNg+ouUhw/ihwP5LN2Rw1c7j1JUUU2XQG+uHxLJ7SO7Ex8e4OyoVSvRxJGcTF5xBQ/OSyEl4yR3jurOb67ph59Xy40BqKiu4audR5m/PoNNh07i7eHGdYmR3DAkilHxIXg2oS/EGMOGgyd4dUUaq/fn0btLAC/ePKj+iqhlp+AfIyA4Gn78Lbhpk4K6SLYaa+Lod/8FpfnWBNJpv4WALpRX1bB873E+2ZLNir3HqbYZJvQOY/boWKb166q1kHauwyeO9z5fwf3vbiK3qIK/3JrItYMdO7N6T04h89dn8OmWbEoqawj29WRyn3CGx3ZmQGQwkZ186OznRVWNjVOlVew7VkRyxkmW7TxKel4JYQFe/GRCPPeN64GXRwMJZ/HPrF/2n6ywNgJS6lKVF8CqP8KGueDhC5OeglEPndl2+HhhOQs2ZfKfDYc5WlhOVCdfHpgYz6wRMWebT1W70qETR79BQ4z7TS/h6+XOG3cnkRjTqdXuXV5Vw/f7cvlq11G+35dHXnHFecu6uwlj4kO5elAENw2LOv8v496lsOB2GPc4XP6CgyJXHVbeflj2G9i/zBrefeUfIGG6tdkUUF1j49s9x3hj9UGSM04S6u/Fj8b3YPaYWIJOD9pQ7UKHThw+kb3NuF/+i3n3jyKyk2NGPjWFMYbsU2WkHi3iaGE5BWVVeLm7EeDtQa8uASR0C2z8F6/oGPxzLARFwI+Xg8fF96EodUFp38JXz0JeKsRPgen/A136nVNko71ZddW+XAK9PbhnbBw/mRh/dqi4atM6dOII7t7XHNi9re1PdKquhH9fD0c2wwMr6/0SK9Xiaqpg05uw8g9QUQwj7ofJvwa/c/vcdmYX8H8r01i64yhBPh48NLkn946Na9E+RNX6OnTiGDpsuNmyOcXZYVy6z5+wZojf/CYMmunsaFRHUpJvJY/kt8A7CKb8BpJ+BO7nJoZdRwr4y9f7WL73OGEB3jw6pSe3j+qu80HaqA6dONrFnuNr/2aNu9d+DeVMx3bBV7+Gg6sgvB9M/wP0nFqvWErGCf60LJX16SeI6uTL49N6c9OwqFZbZUG1DE0cbTlxbHoTvngCBtwEN7+hQ2+VcxkDqUth2bPWRMIek2DSryBufJ1ihrVp+fxp2V62ZRUQH+bPzy9P4NpBES02QVY5liaOtpg4jLFqGt/+zhrVMms+uGuno3IR1RWw8V/Wz2jJcYgdBxOfgvjJZ0ZggZVAvtl9jL98vY/UY0X07RbIE5cncHn/rroulovTxNHWEkd1hdUkkPymVdO4ce6Z8fRKuZSqMtj8b1jzChQdgcihMOphGHDjOaP+bDbDku1HeOXb/RzMKyExphNPXpHA+F5hmkBclCaOtpQ48g/Ah/dBzjYY+zO47Hlw07Zh5eKqK2DLfFj/T8jfDwFdIel+SLoPAs4u61NdY+OjzVn873dpZJ8qY2SPEJ66sg8j4kIucHHlDJo42kLiqCqz/te25mXw8oPrX4W+1zg7KqWax2aD9OWwfi6kfQNuHtD7Cmt3yoTpZ2rOFdU1LNiYyd+Xp5FXXMGY+FAemdJTayAuRBOHKyeO8kKrSeqH/7PaigfOtJZID4pwdmRKXZq8/VYz1vZFUHwUfDrBwJuh/wyrT8Tdk7LKGuavz+Bfq9M5XlTBoKhgHpnckysGdNO1sJxME4erJY6aashYC9sXwu7FUFlkzc6d+BTEjXN2dEq1LFsNpK+AbQtgz+dQXQY+wVZNpM/V0HMKFZ5BfLI5m7mrDnAov5T4cH9+MiGeG4ZE4eulIwmdQROHsxNHTTXk7oXMDXBgORxcDRUF4BUI/a+3ZuRGDXNujEq1hsoSOLDCGtK77ytrRV4EIgZDj4nUxE7g2+J4/r7uKDuzCwny8WDWiBhmj46je6ifs6PvUDRxtFbiqCqHUxlwIt165O2Ho9utiVPV5VaZ4BhryGKvadD7Sqs/Q6mOyFYDmRutCYUHV0PWRqipBAQT3oe8oIF8VxTN+9nh7LHFMLFvJLPHxDGhBTdLU+eniaMlE0dVOZw8aI1+Op0gTj8KsoBa3zefYOg2GCISIWKIVasIiT9nnLtSyq6y1EoemRshOwWykqE0D4Bq8SLVdGdrdSxZPgnEDBjNpAmTiArTnS8dRRPHxSSO6go4vsfa1/vodqupKT8dCrM5Jzn4hljJILSn9Vz74dtZk4RSF8sYKMi0kkh2CrYj26jO3opXVSEAVcadI16xSOQQIvqOwjNqKHQbCF7+Tg68fbiYxNHxlrWsKrP6IA6tsarN2Slgq7LOeQVAeF+r4zqkpz1J9DibHJRSLU8EOnW3HgNuxA3wMgZOZZC3fyMHtq/DdmQrvQ99i2fGxwAYBMJ6IxGJ9pp+olXr9229fXg6so5R4yg7BfuWwZ7FkPadNdpD3K1d9WLHWbNgIxKhcw+dhKeUC6qxGdbuz+Wr9VvIT9tEX1s6o3wzGeSeQWDFsbMFO8edTSSnm479w5wWd1ugTVW1E4fNBoe+t8aX7/kcaiogMAL6XmsND+w+GnyCnBewUuqiFJRVsXRHDh9vzmLToZOESiEzI09wbfhx+nIQz2Pbrf7J04KirCQSNQyiR0LUcPAOcN4X4GI0cSQnW7WLlLch+W1r5JNPMAyeBYNutX5gtEahVLtxOL+UT7Zk8/GWLDLyS/H1dOfKAV25ZWAQo/2ycT+63VrW58gWa4kUAHGDLv0hegTEjLSeQ3t12H7Kjp04hiaa5BevhZR3oLIY4ibAsHug37Xg6bytZJVSjmeMYfPhU3y8OYsl245QWF5Nl0BvbhgaxU3DoujbLQjKTkJWijWiK2uT9bqiwLqAb2crgZx+RA3vMC0SHTtxRHmY5AeCYOBNMPYxq2qqlOpwKqprWL7nOB9tzmZl6nGqbYb+EUHcNCyKGUMi6RLoYxW02ay91rM2WUODs5KtUZUYQKxtm6NHQLdB1usu/ettp9sedOzE0TvCJG/aYI3MUEopIL+4gs+3W/0h27IKcHcTJvQO48ahUVzWryv+3nUGlpadOju3JMueTMpPnT0f0PVsEglLOPvwD2uzTV0dO3G4wpIjSimXlXa8mE+2ZPHJ5myOFJTj4+nG1L5duHZwJFP6dGl4rSxjoCgHju+25nsd32O9zk2FqtKz5Xw61Uokvc++7hxXb892V6OJQxOHUqoRNpshOeMkn28/wtIdOeQVV+Ln5c60fl25dnAEkxLC8fFsZMFFmw0KsyBvn7XkUO3n4lrDg908rXlgtZNJWAKE9bIG7rgATRyaOJRSzVBjM2xIz2fJ9hy+2pnDydIqfD3dGdcrlCl9uzClTxciOzVzcE3ZKchPsyeTWgnlRDrYqs+WC+hWJ6H0tvpTam2I1Ro0cWjiUEpdpKoaGz8cyOfbPcdYvvc4WSfLAOjbLZDJfbowKj6E4bGdCfLxvLgb1FTByUP1E0rePigvOFsuoKuVQLoNPvscEu+wqQQulzhEZDrwN8AdeMMY82Kd897Av4HhQD4wyxhzyH7u18D9QA3wM2PMsgvdSxOHUqqlGGNIO17M8r3HWZF6nORDJ6m2GdwE+kUEMSIuhKHdO9E/IogeYf54uF/CH3VjoCTPGtF1bCfkbLfWzsvdc7aG4ulvrc9VO6F06Q+ePpf8tbpU4hARd2AfcDmQBWwCbjfG7K5V5hFgsDHmIRG5DbjRGDNLRPoD7wMjgUjgWyDBGFNzvvtp4lBKOUppZTVbD59iw8ETbDp0gs2HT1JeZQPA28ONhK6B9IsIJDbUn5gQP2I6+xIT4keov9fFb5FbXWElk6M7ziaTozusDeHAWjYpvI+1vl5ID2vJpNNr6wV0a3INxdUWORwJpBlj0gFEZAFwPbC7VpnrgTn21x8C/xDru3w9sMAYUwEcFJE0+/V+cGC8SinVID8vD8b2CmNsL2vdq6oaG2nHi9mTU2h/FLF8by55xVnnfM7H041Qf286+3sS4u9NiJ8ngT6e+Hq54+Ppjq+nO76ebnh5uJ9nNG9nqm0TqAoaR6W/jcru1fiVZhFSuJfQ4n10LdlH+L71dKr6FHdsZz5ViSen3EM4KZ05IZ3Jl07k0YmTJpBC40OhzZdifCmRi9snyJGJIwrIrPU+Cxh1vjLGmGoRKQBC7cfX1/lslONCVUqppvN0d6NfRBD9Is6dXV5SUU3WyTIyT5SSebKUI6fKOFFSxYmSCk6UVJKeW0xJRTVlVTVnaizNJQJe7vF4efTC2+NaPN3d8PWzEeWWT3eOEW2OEmGOE2pO0Nl2khhbDgOrdxNoK2jweu9dRAyuPcC4ESLyAPAAQPfuOvFPKeVc/t4e9OkWSJ9ugY2WtdkMlTU2yiprqKhuOIkYDO5ugpe7G14ebni5u118f0pNlTXiq6IQKorOPp6/ptmXcmTiyAZiar2Pth9rqEyWiHgAwVid5E35LMaY14HXwerjaLHIlVLKwdzcBB8398bnjLQUd08ICLcel8iRS8VuAnqLSA8R8QJuAxbXKbMYuMf+eiaw3Fi99YuB20TEW0R6AL2BjQ6MVSmlVBM5rMZh77N4FFiGNRz3LWPMLhF5AUg2xiwG3gTm2Tu/T2AlF+zlFmF1pFcDP73QiCqllFKtRycAKqVUB3Yxw3F1VyOllFLNoolDKaVUs2jiUEop1SyaOJRSSjWLJg6llFLN0m5GVYlIEZDq7DiaIAzIc3YQTaBxtiyNs+W0hRih7cTZxxjT+FT3Wtr0kiN1pDZ3SJkziEiyxtlyNM6W1RbibAsxQtuKs7mf0aYqpZRSzaKJQymlVLO0p8TxurMDaCKNs2VpnC2rLcTZFmKEdhxnu+kcV0op1TraU41DKaVUK2gXiUNEpotIqoikicgzzo7nNBF5S0SOi8jOWsdCROQbEdlvf+7s5BhjRGSFiOwWkV0i8riLxukjIhtFZJs9zuftx3uIyAb7v/1C+xL+Tici7iKyRUQ+t793uThF5JCI7BCRradH1rjav7s9pk4i8qGI7BWRPSIyxtXiFJE+9u/j6UehiPzc1eK0x/oL++/QThF53/671ayfzzafOETEHXgVuAroD9wuIv2dG9UZ7wDT6xx7BvjOGNMb+M7+3pmqgV8aY/oDo4Gf2r9/rhZnBTDVGJMIDAGmi8ho4CXgZWNML+AkcL8TY6ztcWBPrfeuGucUY8yQWsNGXe3fHeBvwFfGmL5AItb31aXiNMak2r+PQ4DhQCnwCS4Wp4hEAT8DkowxA7G2vLiN5v58GmPa9AMYAyyr9f7XwK+dHVeteOKAnbXepwIR9tcRWPNPnB5nrfg+Ay535TgBP2Az1h72eYBHQz8LTowvGuuPxFTgc0BcNM5DQFidYy717461K+hB7P2xrhpnndiuANa6YpxAFJAJhGDN4/scuLK5P59tvsbB2W/EaVn2Y66qqzEmx/76KNDVmcHUJiJxwFBgAy4Yp735ZytwHPgGOACcMsZU24u4yr/9K8CvgNMbSYfimnEa4GsRSRGRB+zHXO3fvQeQC7xtb/p7Q0T8cb04a7sNeN/+2qXiNMZkA38GDgM5QAGQQjN/PttD4mizjJXeXWJYm4gEAB8BPzfGFNY+5ypxGmNqjNUUEA2MBPo6OaR6RORa4LgxJsXZsTTBeGPMMKxm3p+KyMTaJ13k390DGAb80xgzFCihTnOPi8QJgL1vYAbwQd1zrhCnvY/leqyEHAn4U785vVHtIXFkAzG13kfbj7mqYyISAWB/Pu7keBART6yk8Z4x5mP7YZeL8zRjzClgBVaVupPI/2/v/lmyCsM4jn+vJVGRUnBriCDaoikCHYRadGhqiQaHXoUEQW8g6DU41BAi4ljuqdgfLKMCoxxS6RU4/Byu+9CZhBuicyO/Dxw45zzP8OO578N1nut+/kT30zktjP0McC8ifgAvyXbVc9rL2d19IumI7Mffor1xPwAOJL0tx6/IQtJaznZEfjsAAAFESURBVM48sCPpsBy3lvMusC/pWNIJsELO2ar5eR4KxxZwrXwq4AL5NnFt4ExnWQMWy/4iuaYwmIgI8r/f9yQ96z3UWs7piLhU9kfJdZg9soDcL08bPKekJUmXJV0h5+KGpIc0ljMixiNiotsn+/K7NDbukn4DvyLiejl1B/hMYzl7HvC3TQXt5fwJ3I6IsXLtd69n3fwceiHpHy34LABfyZ7346Hz9HK9IPuIJ+Sd0yOy3/0G+Aa8BqYGzjhLvn3+CLwv20KDOW8A70rOXeBJOX8V2AS+k+2BkaHHvZd5DlhvMWfJ86Fsn7rrprVxL5luAttl7FeByUZzjgN/gIu9cy3mfAp8KdfRMjBSOz/9zXEzM6tyHlpVZmb2H7lwmJlZFRcOMzOr4sJhZmZVXDjMzKyKC4eZmVVx4TAzsyouHGZmVuUUMCmMOmOl1jcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 限定范围\n",
    "df.plot.kde(xlim=(0,80))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    55\n",
       "0    45\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age=16\n",
    "young=data_train[data_train.Age<=age].Survived.value_counts()\n",
    "young"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    504\n",
       "1    287\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old=data_train[data_train.Age>age].Survived.value_counts()\n",
    "old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>45</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>504</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0    1\n",
       "young   45   55\n",
       "old    504  287"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame([young,old],index=['young','old'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=[\"survived\",\"death\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>45</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>504</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       survived  death\n",
       "young        45     55\n",
       "old         504    287"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c45c278>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c6d53c8>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['p_survived']=df.survived/(df.survived+df.death)\n",
    "df['p_death']=df.death/(df.survived+df.death)\n",
    "df[['p_survived','p_death']].plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析票价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      32.204208\n",
       "std       49.693429\n",
       "min        0.000000\n",
       "25%        7.910400\n",
       "50%       14.454200\n",
       "75%       31.000000\n",
       "max      512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Fare.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "survived=data_train[data_train.Survived==1].Fare\n",
    "death=data_train[data_train.Survived==0].Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame([survived,death],index=[\"survived\",\"death\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71.2833</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.1000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.4583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>51.8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16.7000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>29.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>35.5000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>263.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>25.9292</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>NaN</td>\n",
       "      <td>69.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>13.8583</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.4958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>52.5542</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.8458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>83.1583</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.5167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>NaN</td>\n",
       "      <td>29.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>30.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>30.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived     death\n",
       "0         NaN    7.2500\n",
       "1     71.2833       NaN\n",
       "2      7.9250       NaN\n",
       "3     53.1000       NaN\n",
       "4         NaN    8.0500\n",
       "5         NaN    8.4583\n",
       "6         NaN   51.8625\n",
       "7         NaN   21.0750\n",
       "8     11.1333       NaN\n",
       "9     30.0708       NaN\n",
       "10    16.7000       NaN\n",
       "11    26.5500       NaN\n",
       "12        NaN    8.0500\n",
       "13        NaN   31.2750\n",
       "14        NaN    7.8542\n",
       "15    16.0000       NaN\n",
       "16        NaN   29.1250\n",
       "17    13.0000       NaN\n",
       "18        NaN   18.0000\n",
       "19     7.2250       NaN\n",
       "20        NaN   26.0000\n",
       "21    13.0000       NaN\n",
       "22     8.0292       NaN\n",
       "23    35.5000       NaN\n",
       "24        NaN   21.0750\n",
       "25    31.3875       NaN\n",
       "26        NaN    7.2250\n",
       "27        NaN  263.0000\n",
       "28     7.8792       NaN\n",
       "29        NaN    7.8958\n",
       "..        ...       ...\n",
       "861       NaN   11.5000\n",
       "862   25.9292       NaN\n",
       "863       NaN   69.5500\n",
       "864       NaN   13.0000\n",
       "865   13.0000       NaN\n",
       "866   13.8583       NaN\n",
       "867       NaN   50.4958\n",
       "868       NaN    9.5000\n",
       "869   11.1333       NaN\n",
       "870       NaN    7.8958\n",
       "871   52.5542       NaN\n",
       "872       NaN    5.0000\n",
       "873       NaN    9.0000\n",
       "874   24.0000       NaN\n",
       "875    7.2250       NaN\n",
       "876       NaN    9.8458\n",
       "877       NaN    7.8958\n",
       "878       NaN    7.8958\n",
       "879   83.1583       NaN\n",
       "880   26.0000       NaN\n",
       "881       NaN    7.8958\n",
       "882       NaN   10.5167\n",
       "883       NaN   10.5000\n",
       "884       NaN    7.0500\n",
       "885       NaN   29.1250\n",
       "886       NaN   13.0000\n",
       "887   30.0000       NaN\n",
       "888       NaN   23.4500\n",
       "889   30.0000       NaN\n",
       "890       NaN    7.7500\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.T\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      32.204208\n",
       "std       49.693429\n",
       "min        0.000000\n",
       "25%        7.910400\n",
       "50%       14.454200\n",
       "75%       31.000000\n",
       "max      512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Fare.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11cb819b0>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.kde(xlim=(0,512))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 组合特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fare')"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 同时查看年龄和票价对生还的影响\n",
    "age=data_train[data_train.Survived==0].Age\n",
    "fare=data_train[data_train.Survived==0].Fare\n",
    "ax=plt.subplot()\n",
    "plt.scatter(age,fare,s=10,alpha=0.3,linewidths=2)\n",
    "ax.set_xlabel('age')\n",
    "ax.set_ylabel('fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fare')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kJ+ruteMYFu+8I4kMw0FfR+7EyzP1VmbwvRQsupEWfarqX/s+QChV3gzT5RIMYApmR00yW7mtVupmU1N9qupfGiBUT+pkJXX5d20kMgCMBH07+t5WbquVeqHSX4ND/9FfTPWcTjYXLf+uWw8OshbLsBZPc+vBcNPf28pttZI2v1U7pU8Qqud0spK6/LvOLkYZC/sAOLsYa/p7W7mtVurnSn/VXRogVE/qZMVm+Xc51Rt0a1utpBXFaifEGNPtNOzY8ePHzYkTJ7qdjLZpplJvr1YAdnK/yr8rlswQHvC1ZFu99Nv0UlpU94jISWPM8Xrr6RNED2qmxcleHwitk5mZx+1iYSPBUy9fZT2eYTTk4yN3TTMzEtzRtpxed1svpUX1Pg0QPaiZFie90DqlUrfuUlvxvU+9fJUTF9a2HM/fed8NrUieUn1HA0SPaWaYid0MSdGOTLwdTzONpLNV35vK5FiPZ1hPZLnn8AgnL22wHs+QyuQI+GpfKlp0o/YiDRA9ppkWJztpnVLMTMt7KLeqSKqVTzPNZPqt+t6Az8NoyOqzcPLSBqNBL6MhX83gsNeL+NT+pgGiBzXT4qTZ1inPnV/lR68tsxZPMxbyE8/k+KXbDu46za0eYK+RTL/Yjr+V3/uRu6YBttRB7DadSvUrDRA9qJmhCZpZN5cvcPbKJhdW4wz6vVxYjTN5xc9Dx6Z2XTzSyrb29YJN5V27yyUta+M/MxLkd953Q8PFSv066qxSjWhbgBCRPwU+AiwZY263l40BfwnMAReAXzfGrIuIAF8APgwkgN82xvy8XWnrF81kMo2uW2zWbLXQf/t9K7SqrX29YFN5135oPMgt00MtbeNfLzg0kk6l+l07nyD+LfCvgK+WLXsCeMYY8zkRecJ+/xngl4Gb7L/7gC/a/6oW8rhdvGNmmJVYhrVYhrnxEO+YGW5ZhtbKQdmqBRunu/bZUcN9148BnW/GqR3Q1F7WtgBhjPmRiMxVLH4EeNB+/RXgB1gB4hHgq8a6nX1OREZEZNoYc7Vd6duv7js6TtDnaesw2q3IpKsFm167a9eRStVe1uk6iANlmf4icMB+PQNcLltv3l62LUCIyOPA4wCHDx9uX0r3qH7L0JzS2I25Iurph2PZiH45L1RndK2S2hhjRKTpAnBjzJeAL4E11EbLE7ZP1MoE2plJdDMDqvfd+3loE22uq5x0OkBcKxYdicg0sGQvXwAOla03ay9THdTOTGKn23bKiJttWlrvu/fy0CaNBjJtrqucdDpAPAk8CnzO/vc7Zcs/KSJfw6qcjmj9Q2s1klG0M5NoVaa+dc6FMGcXY3Wblha/ezWWYdwegrv8u/t9aBMnzQQyba6rqmlnM9e/wKqQnhCReeCfYAWGr4vIY8BF4Nft1b+L1cT1HFYz14+3K1271W8XTaMZRbsyiZ12Zitl6vE04yE/YGXEHrcLcQkbiQx/ffoqYyE/4pKq28nlCyxvpjg1H2F6OMCp+QjjIV/pu5sJOLWOEfRWPUQzgaxVFf/9dm2o+trZiuk3qnz0sMO6BvhEu9LSCv1WtFDUTEbRytZBlcdLqnRmq5YRL0VTnF6IMBn2c3ohwlj47Uxdiisa65VQncft4lo0TSKT48TFdUaDXq5F06XvbCbglGekpxY2GA/5cbmE599aa8vQJTu1k2C/m+a6rbg2Wlk/pFpHe1I3qF+KFso1klGUX9ziEg6PB5kdrT3BzU6Kqw5XdGabmwjy7LkVxyIkj9vFpbUEl9YSnJ6PMBz0cmktUQoohYJhJOjjPUfHObsYpVAwVQMNwNSQn5Dfw9HJENc200wN+bes30zAmZsIcmYhQqEAuYJhNZbmhwutH7pkp4r7VRnIqgX74vrF1m21epBX+913c23stH5IA0ZnaIBoQL+W0TZSdFB5cd8yPcR914857tduiqsqO7M9e25ly/cmMjmCPg+xVJYBn5vNZJZMrsDQgJdMrkA6m9+W+Z1djDruU2U6Q34Pd8wMsxpPc8fMMFODgS2T+TQacAAurCTwuASXCC7ghbfWuRZNtXzokmZV7vNE2EeuYEqBbG4iWHP9uYkgF1YSjr9trd99t9dGtaLEys+L50k8kyNknyf99CTfrzRANKDXOmc1o1bRQbWLu5pG7xTrHS+n742mIgT9HjaTOYYGPKzHM4wEvYR9bmKZPL6yY12vOKTe00v5+o0EHKfj9Y7rBnnlSpR0rkDBmLYMXdKMyn3OFUwpkHlcwoWVxJaJjyrXP7MQweMSx9+21u++m2ujvCjxwND2okTH8zO9SdDrJpLK9c2TfD/TANGgfh1SoVbHuGYu7mbvFIvHZy2WZizsr5opn7myyXDAQyKbZzOZK2374HCAgNfNRjzL3Hhgy5AgtfZpJ0NxOP221Y5XZX3F4fEBRoLetgxd0qjKfT61sEGhAC4R7pwd3vZbNbN+8bjU+t13em143C6WNtPE0zlOXkwyGvSytLm1fqj8PBka8JBI54ikcn31JN/PNEA0qN96IFfabeVkrWDidEyiqSxnFiKsxtKMh/3MTQS3FAVUfm88k+PyaqK07UPjQUJ1hgRp1RwZ5b9tIpuvWYxWWV9x7OAQE2F/V3t1V+7zeMhfeoJwOgbNrl/veO702sjlCxwY9BP0ebhhMszVSIoDg/6awSeRyXGp7Dzplyf5fqUBokl77WRs5uKuvFhrVTQXp+5ci2cYC1l9D8qn7qz83s1U1rFseScBeTd3tKffWqtanOJUXxHwuLo2UGC5yuFHnOoUnNavVQdRbd1aNxHN8LhdTA4FuHN2mNVYhjtnh5kcCtQMPpupbKmuqp+e5PuVBggFNHZxV16slRXNxQrEtViaFy+tc34lzg0TIS6sJjg0GnRsIVOv2Ggnme5u7mhrFac0U1/RbYN1joHTMZoZCTqu386n52aDT78/yfcbDRCqaY1UIF7bTJPK5jm9EGEw4CGZzTc8x0Ir09ns+vWKU3q1LqpaRXK9Y+BUz9JJO83wNTh0hgaIfaLyAtztHVitCsRbDw5yZmGDRMbD4bEgiUy+asVvvXQ00y6/8n0js8JVqhcAqmVorbyjbeWTD+w+M+1EJ1HN8HuTBog9rpn27s2qLPcuViCeXYwyOxpkdjRI2O9mcjDAkfHQlgrteh2gFjYSPPXy1VIl90fumi4106y3TxNhHz94bXnLvNLVik/K5fKFhu9onToa7vZ47nRbTk8+YvfwbkW6+rGTqGoNDRB7XDPt3XfrtuuGShWILpfVM8AUjGOFdrHVklNHuXDAy7Pnlnnu/BrriQyjQR+pXJ5PP3xzQ/t0binGSixdep/O5bnnyFjd3rq1hstoZS/idmzLKVjvNlPf6Thaau/QALGHbWvvPh+hYAwuF9w5M7Lri73aKKnl2ysW85RXaBf7PWTzxrGjXNjv5qfnVzm/HMfvFtbiGUZDXlKZHB63q+Y+vXR5nbV4hrV4huNHRjl5aYPXF6P43C5i6bxjZvn8+VV++Noya7EMY2Erc/2gPVxGK3sRt3Nb5T2l84UC8VRux5l65bZdVcbRUnufBog9bFt7d3v4hWrt3ZtRb5TU8kxmwOdmLZbZkmF5XDA04HHsKHdqYYPVaIZ0Nk8qa3XguhZJb2tJ5LRPk4MBIskcBWM4eWmDkQEvfq+bWDrvmFnm8gVeWYhwYTXOkN/DhdU4Ews+3m8Pl9HKXsTt3FblU1SuYHacqVdu+1CNnuhqb9MAscc10969GR537VFSnYZ+KM+wDo8HtxQplXeUGwp4GQ/72EhmCXhcpHIFDpQNsFdvn95708SWOogDwwFWo+mqmaWI1enN2N3giu8buatvtFVTO7fl1BP6hqkQ4yE/yUyuqd95Jz3R1d6lAaKOfi9rbaa9ezNy+UJplNS5iSDL0UxplFTYXm7tlGGVV0pXdpTbTGUREdbjaWbHgtx5aKTUIqmRfTp2cKhUvOVUtFPkcbu49bohlqNp1uJp5sZD3HrdUMt7ETfyhLDTbTn1hJ4aDOy4+Wi/jjumWk8DBM5BYK8PM7zbffC4XYT9HsJ+D5fXkkyE/YT9nqoZa7UMq1oHqLmJIMMDvi2tmOrtQ+X7WgGl3P1Hx6sO67HTXsRO39XKHsnVnqIq92Env3Ov9vVQnbevA0St1iuVRSSVrWzaMSlKO7SzDXtx3FKXbH0P1TOZRjtuzYwE+Z333cBGPMVIKNCS9Fb77loBpNmOXLWOdyt7ATsNQdEqu+mJvhduntTb9nWAqNZ6pd5w1LWaDTbzNNIJ7WrDnssXMKVxicY4uxjDlM2jsNvMsFY/iFZpZliPnVbwwvbj3Y7e4u34nRtNZ7/Otqjq27cBol7rldKMXPMR6wmiYjjqyp6qtS6STnQ0anTo60aaOzaSqW8dlyhWtay6XtFLtfffODHPD19fZi2WYiwcIJXN8+kP3NxUOnfS3LRR9Y53vfmtW2m3k/ZUbqvZ/6Md6bqjE09s+zZAgNVaxRizrfUKlE0taQwF+/1KNLOtp2qxeKqy4xe83SegnR2NamV2zVY4NptxVnbOciqrrtZzurLlUfl7n9vF829a/SY8LmEpmiHsc9esdG5mmsp2TZHpcTc+v3Ut3ahY3mnQbPf5rbbr5BPbvg0QiWweEXC7hNV4miMTQW69bqh0YhenlgTwuISgz8Mt04HSj7IcS/Oj16y5iEeCPoaDXtzi2nKRFDO00tNInfmBd6JeZtdMhWNxW0ubKaaGAtu21YzK+p3iMBzFTnWV7fbL3w8NeHhzNUEmX7D6Qbjg0noSj9tFKpOrus+VAaA4jWVxlNlIIkM44Cn1ybj14CBnF6OlDC2XL5QqtmPJDOEBX9PHu5H5rauNEVXtwm9kTKlqEzQ1+sS206DZTHDSoNEanXxi27cB4vR8BLcIo0E/XpcwGQ5w9somP7+wznDQi9/j2jYV4vuPTZUu1j/+/jkurMYZ9Hu5tJbgQC7AsYODnLmyyYDPzYXVBLy+XBobqNb8wDvVyN1bo3UBqUyO1xc3+ebP50sdyD56z2zV+amLx7Daifrc+dWyAOon4HOxGs0wPRzgpcsbTIatZXfOjGxrx//zS2u4MOQL2MN1AMbwxe+dI5rJsRqzMvr3HB3nzJVNlqIpfvT6MslMjgGfm/n15Jbf7kokSTKTZyOeZSzsY3jAQySZ4/87dYWJsJ/1RJYv//gt1uMZcoUCS5tp0rkCYyEfv/3AHMcODjV0vJ3miyif37pYr1I5RlS147kcS3Mtkqq6frnKCZomwj5Oxxp7YnPqyOj0FFDtHKp3E6J1FK3T6Se2fRkgigc5mS3w8C1TvHp1k8VIkkgqR6RiTt/iVIhvXIuWMpHhoJfNpNVqxBirHmJq0MctBwdJZvJcWE2Qyxc4vbBZc37g3fyoxf/b6N1bI8VK337pCoubKVLZPANeNz96Y4WPv/dozWNYbQTRs1c2ubAaJ+TzcGktjgB+j7vUqS6ZzTMzOuDYjv/A0ACJbKHUKsoAS7EUp65EWE9kKRjDaNBXeiJb2kyzFsuUnhjOLcVK01gOB72sxNIApbqmwYCHVLZAJJklmzdsprIkM3lW4xmubaZIZ/N43S4GA9bl8X/9/TtLx7DW8d5aL/P2fBFFxUmUygNqcRIlp+P56tVNIoksG8nt61eq3PaFlTg3ToWr9rRupud1vQy+3k2I1lG0Tqf7qezLAOFxW71zT89v8B9PXyHgdePzuDAI982NcuLiOiMDXnweF+86PMK1zTTz60leX4yxnrBmSBvwufF73CxFrbu7W6aH+bs3W08YvL7M6YXNqr1cy+94m72jqjYGz07brJcXK8XTOTK5AjdMhplfTyJUL2qpdaLm8gVS2RwbiSzRZI5cocDB4QBBv5sbpkJc20xzx+wws6MDxFK5bTOguQVS6dyW70tmDCvRFPdeP85Pzq8RT+fI5gpk8gX8XuHUZeuJ4eX5DQYDXgJeN8ePhLkSSeJ1CdFUFoNV57SesL5jKuRnM51hKZpkLZ5lNOhhNZZGgPuOjvHaYoy1eGbLMah3t1z+ucslxDM5njl7DZ/HzeJmivVElnsOj3Dy0gbr8UzpibTyeIb9btLZPBtJ5/XLpTI51uOZ0rZPXFzHJcJyNMXdh0a3nYNOY3LV6nndaAZtPSIyAAATc0lEQVRfrVhJ6yhaq5P9VPZlgAB4bXGTN5ZibCYzeNxuhgMexgf9nLy0wVjIx9CAlwGvm4urCUZC1l3o5fUkR8aCXFxLcGgswOTgAF63MBH2M+BzA2ypc6jWy7V0x1sxyF0j6pW/FzVyAZZfvO88PMqPz60Q9ru5FkkyEfZxYChAeMDXdNGCx+0im4f1RIZYKkc44OHweIi7ZkdYjae5Y2aYiUE/btfb2yyfAW0lligW4ZcIEPZbTyBetxDye/C63fjcLt5YjG2Z+D7k83D3oRFWYxnumh0hlsmxvJlmJZbmyHiIqL3PLhe4XC6iqTzpbJ63VjIUjPVdL12OEPJ7CAc8WwJkvbvl4uepTI6Tlza2/FapjDVY4MlLG4wGvYyGfFsy+8rjmckXSOcKVdcvCvg8jIZ8pW2PhXyMhXxMDgYcz0GnMbmqdWTc7VwT2jO79To5q96+DBDLm0lOXFgnkswAkM3lSeWF6UE/06MDTA4G8HuE//LGKuuJDLmCIZHJgzGcno8wOOAhkzVMhP38naNjvL4UL/UBgNrjHw343CysJXnurTXHQe5qaeRurNHy3soiqlMLG7z3pglevxbD53YxFvLxa8dnHeecLqp2oqYyOS6sxogks+TzBSJJQzKT5aYDYWYzA4QD3qrDUefyBQ4Ohwn6PWQSbz9FhHwuZsfCrMTSJLN5Dgz5uWkqxBvLMQJeNwNeN3OHQixF09w5M8zM2ADTw9bTyUjQy4/fWCHoczMe9jM04OWVhQhL0RSHRgZwC7y5XGBmZICLq0lcbsOg38NYKMC750Ydf4t6RXZrsTTnV+LEMjnuOTTKmSub3DkzzEYqy2oszeRgYFvvcKfe5H6Pu2Zv8qLiZ8X6igePTbISyzRUB1GrI6NTBt/sXBPaM7s9OhFk92WAOLsYI5nNkytAPl8AgbCB8ICXu2ZHGA56ee78Kulcnqmwj2g6h88tuF0uDgz5yRbguuEBVuNp/uZMjLGwj1SusO2iCXrdW3oGFy/8f/3DN6sOcldPrWKdRlqjVLYuKlagZ3IFJgcD/LNfnSPkdTESCmybc7q4rWodzIrbXo+l+cXVKOlsHgFyhTxXNlLcO/d2hfczZ69tCXSXVuKcmt9gLZZmYjBAKrW1iCmdK3DjVJiw383VSIqfvbXGT99cZSTo446ZIYYGvFxeSzAx6Gcg4MHtcpHI5rm8ECGSzLCZzOH3ukot0/IFQ8EYEOHGA4P4PG5Woimmh30ksgWOToZIZwsUME3dqZV3vkznC4yHfKXfKhjwsFlRdOak+F2DAS+3zwyzHkszGvYzWCMTLvY8Ly+COsbWJ8mZkeCW4rJG68EqM/hm55poxR3vTp6Q1e7tuwCRyxdIZnJMDfpZjWdY3kwR8Lpwu11E01nOLm4yEvSxGEkBVrmtiJDJFRjwutlIpBkJ+Unl8gx6vCBWVeqF1Tgr0XTNi6aYkRcHuTs6aZXHT5WNVOqk/KnA5RIOjQeZHi6Uyu6Ld/kDPg+rcSsNN0+FeH0pXnrCSGVyhAd8pdZFK7E0E2E/fq/VpHc5mmQ05CeSzHBsapDRsL9sW2FeX4qV6k4iiUypKeVQwMtKNMHEYLAUnK5tpkjl8hQMuF3YTT5ly3EoBrqXLq8zORjgp2+tctaulA36PKQKW49BpgDffXme4ZCfl+cjpLL50rHxuOHYgSEKWP/p0mqc1Wiak5fWSOcM0WQGn8fN8SOjrCeyvDy/wfxako1klrV4lncdHuX43Bjr8Qw/Pb9KKp/lF1ejDAY8nL68yT+4t35GVHx6LO98GUvnGB7wcNv0IMNBHycurnFmPmKfIwnAudK5WnNdp3OqUmURVPGcqtaCqpFMtjyDh+3BvdE6hZ1k6O2cEVHVt+8CRCJrtTIa9HvwuITRoA+PWxgd8LIRz3LnnVal3cHhAAGvm414lsNjQdYSGebXEgyH/NY8BbkCMwMe3nN0jFeubpJM50lnCqWK6GoXjcftYmowwB0zw6Xy+KnBQM2Lp/ypYMBrzYlwaHQAgFevbHKprIPeajzNa4sxfvLmMqNBP2G/m//j22dYi2cYsQPXK1cieN0uLq/FSWQLrMetorZzS3FevLTOeMjHSNDPsQNhlmMZfnJuhdGQj0gyw5WNt5td3nwgxE/eLHYW9PJ3jo5xJZJhatCHWwSXWPHT63ExPeznZxfWS5WgA14X55asSuDVeIYzCxEurSYoGEAyjsfhzZUYqatRMnlr/KfZ0QAr8SzXNlIIwvCAhzeXY2zE07y5EieZyRNL57hteoi8Mby+GOP2mSGubaZYiqWZDPtYiqZZjCT5X3/pZlLZHOlcgWfPrXDTTJgLqwkCXnfNfgiV815k7EzUIHjdwpHxEA/dMgXAf3l9uWol9fZteZjfiG9rar3TO+daLagaVfzeTtYpdHJGRLVdTwUIEfkQ8AXADfyJMeZzrf6O0/MRq08CcHAoQDSV4/6j47y2GGVq8O2mk4fGg2Wdq7z89PyKVeHqcxPL5An7PIwO+Di7GHOsiK510TRTJltZ7/AfzyzicwuRZIaRoI9EOkeuQOlubn49yXo8QySZA4SnTl0lk8sTtSuLs3mr37hIgXw+z3oiSzpvcIshmTWYLGDSRJI5VmMpJgYDRJJZELiykSCVLeASYT2R4eSldbLZPOl8geVomqXNDLfPDPHi5SQDPg9DBYMLGAn5GA76eONadNu0oGvxDNl8gSvrSXLl7VodZPOC3+smnc9RADaSOXxuF+mCYT2R4fJagsGAhwsrCbL5PLF0HrfA+dU4D948xUTYx+2zI/ziWpSlTWsfvS5rmx63ixFfgIPDAY6MB1mNZzgyHrRuFGp0UqvMwPxeN3PjoS1Dhxf/f3lFslOlc+W2ypvrFhs37CQjrmzlVKtFVCM6VafQyLwX2iKqvXomQIiIG/hj4IPAPPCCiDxpjHm1Vd9R6v+QyfPh26f57pmrHBlz4fe4eODGcaticDzoODxDKpcnmsyVLvx3Xz/KRDhQt9LPSTNlspXjQvncQrZgSs0TvW5hOOAuNY0sFAwul/DQLVOcuLhe2t87D41wej5CwOtiwOci4PWQygqpXIF8Kofb5UKwim1Ggz5WYhm8bsEYeOiWSU5e2iCTK1j1MkMBrkUSpDM5sgXDzHCA+Y0UA/kcbpfwzkMj/PzSOmMhH3fNDluttUJWkdU7poc5vbCxZVrQ599apRgVXEChyrHIZguE/G78HsEtwsiAh6EBH2G/h5V4httnhnlrOY7bZUhmC9wwFeTiapLRAS8PHpviniMjjIb8rCUyxNN51hNpRoN+/qubJ0u/Q2Vlb62KYadGAzdMhXjnoVFiqey24UdqbdspMyxWvt9wJMzVSIoDg7WLIqupbOVUq0VUIzrViqaygrzZGzG1ez0TIIB7gXPGmPMAIvI14BGgZQGi8oQ7dnAIj0u2BIXyiuXi/wHnOQMq54dotvNbo+uV37FdWLU6NRUvkOKTTmXTyBcvbzAR9lEwBp8nx5mFCEMDHg4MBTgyHmIlmmZi0Mq031iKEU1mMUDBFIikcoyEfIzaldgvXrZaWoV8buKZPJFElumREAWSxJJZFiIpAh4XQwN+3j03xmo8za3TVu/jAa+Huw4FiaVzLEfT/PWpq4yFfYQDntK0oJODAdbiGVZiGVLZAkGvi0R2e5g4MhViM5njupEgt88M8dCxKSaHAqWy/bV4husnrZZMQV+O1WiW64YD3DYzzAduO1DazkO3TDEZ9m8blqL4G1ZW9tb6/Rqd96Leth0zwwHD7OgAq7EMd84OMzlUuyiylmYCX6M6kTG3a0ZE1ZheChAzwOWy9/PAfa3+EqfH48qg4KTaXZNTHUOrlX93IpuvORBdsWlkMSO4bXqQ755eZC2eKTVdTWYLW1ox/eC1ZZajVqX8UjRNMpNnIuznw3cc5NWr0dK23j03ygsX1kvNLg8M+vizn1wsbet/evAo4YCPWCqLuAQBCgVDOOBlJZZiOZouVeq/e26USDJX2vZH75nhay/MsxpLMR4OsBpNcupKrHQM3n14iA+84zpWolax10fumuaAXXczNxEkUGOff/uBuYZ+y3KN3l3vZN6LattuZ2bYTODrJe2aEVE1RoypUuDbYSLyUeBDxpj/zn7/W8B9xphPVqz3OPA4wOHDh++5ePHijr6v30+wRsZWKs8IKntEO/VdqDZQXeW2Kt8XWzE5bbu85cvphc3SMNh3zAzx8K0HtgyQB2ybIOjpV67wwXdcV/W7m9nndmrl+aRNOlW7ichJY8zxeuv10lm3ABwqez9rL9vCGPMlY8xxY8zxycnJHX9Zv19w9dJfmYlWZpSV/798/cp1K7dV+b48OFRu2+N2VYxRtHXuiMptVc4eVx4cnL671medCg7QnkmA2rFtpZrRS8+aLwA3icj1WIHhY8A/6G6SVKtob1ql+k/PBAhjTE5EPgn8J6xmrn9qjHmly8lSLdLJ8WOUUq3RMwECwBjzXeC73U6Hah8NDkr1D71alVJKOdIAoZRSypEGCKWUUo40QCillHKkAUIppZSjnulJvRMisgzspCv1BLDS4uS0gqarOb2aLujdtGm6mtOr6YLdpe2IMaZuT+O+DhA7JSInGulm3mmarub0arqgd9Om6WpOr6YLOpM2LWJSSinlSAOEUkopR/s1QHyp2wmoQtPVnF5NF/Ru2jRdzenVdEEH0rYv6yCUUkrVt1+fIJRSStWxrwKEiHxIRF4TkXMi8kSX0/KnIrIkImfKlo2JyNMi8ob972gX0nVIRL4vIq+KyCsi8uleSJuIBETkZyLysp2uP7CXXy8iz9u/6V+KSOcmgdiaPreIvCgiT/VKukTkgoicFpGXROSEvazr55idjhER+YaI/EJEzorIe7qdNhE5Zh+r4t+miPxut9Nlp+1/sc/7MyLyF/b10PZzbN8ECBFxA38M/DJwG/AbInJbF5P0b4EPVSx7AnjGGHMT8Iz9vtNywO8ZY24D7gc+YR+nbqctDTxkjLkLuBv4kIjcD/wh8HljzI3AOvBYh9NV9GngbNn7XknX+40xd5c1h+z271j0BeBvjDG3AHdhHbuups0Y85p9rO4G7gESwLe6nS4RmQE+BRw3xtyONR3Cx+jEOWaM2Rd/wHuA/1T2/rPAZ7ucpjngTNn714Bp+/U08FoPHLfvAB/spbQBQeDnWHOWrwAep9+4g+mZxco4HgKeAqRH0nUBmKhY1vXfERgG3sKuA+2ltJWl5ZeAZ3shXcAMcBkYw5qi4Sngv+7EObZvniB4+yAXzdvLeskBY8xV+/UicKCbiRGROeCdwPP0QNrsYpyXgCXgaeBNYMMYk7NX6dZv+i+BfwwU7PfjPZIuA/ytiJy053KHHvgdgeuBZeDP7GK5PxGRUI+krehjwF/Yr7uaLmPMAvDPgUvAVSACnKQD59h+ChB9xVi3BV1rYiYiYeCvgN81xmyWf9attBlj8sZ6/J8F7gVu6XQaKonIR4AlY8zJbqfFwXuNMe/CKlb9hIj83fIPu3iOeYB3AV80xrwTiFNRbNPN898uy/8V4D9UftaNdNl1Ho9gBdbrgBDbi6fbYj8FiAXgUNn7WXtZL7kmItMA9r9L3UiEiHixgsO/M8Z8s5fSBmCM2QC+j/VYPSIixZkRu/GbPgD8iohcAL6GVcz0hR5IV/HOE2PMElZZ+r30xu84D8wbY563338DK2D0QtrACqg/N8Zcs993O10fAN4yxiwbY7LAN7HOu7afY/spQLwA3GTX/PuwHiGf7HKaKj0JPGq/fhSr/L+jRESALwNnjTH/olfSJiKTIjJivx7Aqhc5ixUoPtqtdBljPmuMmTXGzGGdU98zxvxmt9MlIiERGSy+xipTP0MPnGPGmEXgsogcsxc9DLzaC2mz/QZvFy9B99N1CbhfRIL29Vk8Xu0/x7pVCdSNP+DDwOtYZdf/e5fT8hdY5YlZrDuqx7DKrp8B3gD+MzDWhXS9F+sR+hTwkv334W6nDbgTeNFO1xng9+3lR4GfAeewigT8XfxNHwSe6oV02d//sv33SvF87/bvWJa+u4ET9u/5bWC0F9KGVXyzCgyXLeuFdP0B8Av73P9zwN+Jc0x7UiullHK0n4qYlFJKNUEDhFJKKUcaIJRSSjnSAKGUUsqRBgillFKONEAopZRypAFCKaWUIw0QSu2QiHzbHgjvleJgeCLymIi8bs9d8W9E5F/ZyydF5K9E5AX774Hupl6p+rSjnFI7JCJjxpg1e+iPF7CGYH4Wa1yhKPA94GVjzCdF5N8D/7cx5scichhraOZbu5Z4pRrgqb+KUqqKT4nIr9qvDwG/BfzQGLMGICL/AbjZ/vwDwG3WUDoADIlI2BgT62SClWqGBgildkBEHsTK9N9jjEmIyA+wxsqp9lTgAu43xqQ6k0Kldk/rIJTamWFg3Q4Ot2BNzxoC3icio/YwzH+/bP2/Bf7n4hsRubujqVVqBzRAKLUzfwN4ROQs8DngOazx+P9PrBE2n8Wa8jNir/8p4LiInBKRV4H/oeMpVqpJWkmtVAsV6xXsJ4hvAX9qjPlWt9Ol1E7oE4RSrfVP7XmzzwBvYc11oFRf0icIpZRSjvQJQimllCMNEEoppRxpgFBKKeVIA4RSSilHGiCUUko50gChlFLK0f8PoHycUZqhAoEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age=data_train[data_train.Survived==1].Age\n",
    "fare=data_train[data_train.Survived==1].Fare\n",
    "ax=plt.subplot()\n",
    "plt.scatter(age,fare,s=10,alpha=0.3,linewidths=2)\n",
    "ax.set_xlabel('age')\n",
    "ax.set_ylabel('fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fare')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=plt.subplot()\n",
    "\n",
    "age=data_train[data_train.Survived==0].Age\n",
    "fare=data_train[data_train.Survived==0].Fare\n",
    "plt.scatter(age,fare,s=10,alpha=0.3,edgecolors='gray')\n",
    "              \n",
    "age=data_train[data_train.Survived==1].Age\n",
    "fare=data_train[data_train.Survived==1].Fare\n",
    "plt.scatter(age,fare,s=10,alpha=0.3,edgecolors='gray',c='red')\n",
    "\n",
    "ax.set_xlabel('age')\n",
    "ax.set_ylabel('fare')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 隐含特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                            891\n",
       "unique                           891\n",
       "top       Ryerson, Miss. Emily Borie\n",
       "freq                               1\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Name.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train['title']=data_train.Name.apply(lambda name:name.split(',')[1].split('.')[0].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr              517\n",
       "Miss            182\n",
       "Mrs             125\n",
       "Master           40\n",
       "Dr                7\n",
       "Rev               6\n",
       "Mlle              2\n",
       "Major             2\n",
       "Col               2\n",
       "Capt              1\n",
       "Lady              1\n",
       "the Countess      1\n",
       "Mme               1\n",
       "Don               1\n",
       "Jonkheer          1\n",
       "Ms                1\n",
       "Sir               1\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.title.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## gdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train['family_size']=data_train.SibSp+data_train.Parch+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     537\n",
       "2     161\n",
       "3     102\n",
       "4      29\n",
       "6      22\n",
       "5      15\n",
       "7      12\n",
       "11      7\n",
       "8       6\n",
       "Name: family_size, dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.family_size.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(family_size):\n",
    "    if family_size==1:\n",
    "        return 'Singleton'\n",
    "    elif family_size<=4 and family_size>=2:\n",
    "        return 'SmallFamily'\n",
    "    else:\n",
    "        return 'LargeFamily'\n",
    "\n",
    "data_train['family_type']=data_train.family_size.apply(func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>title</th>\n",
       "      <th>family_size</th>\n",
       "      <th>family_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>2</td>\n",
       "      <td>SmallFamily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>SmallFamily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "      <td>Singleton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>2</td>\n",
       "      <td>SmallFamily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>Singleton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked title  family_size  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S    Mr            2   \n",
       "1      0          PC 17599  71.2833   C85        C   Mrs            2   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  Miss            1   \n",
       "3      0            113803  53.1000  C123        S   Mrs            2   \n",
       "4      0            373450   8.0500   NaN        S    Mr            1   \n",
       "\n",
       "   family_type  \n",
       "0  SmallFamily  \n",
       "1  SmallFamily  \n",
       "2    Singleton  \n",
       "3  SmallFamily  \n",
       "4    Singleton  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Singleton      537\n",
       "SmallFamily    292\n",
       "LargeFamily     62\n",
       "Name: family_type, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.family_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据图形分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签\n",
    "# plt.rcParams['font.family']='sans-serif' \n",
    "# plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2) # 设定图表颜色alpha参数\n",
    "\n",
    "# plt.subplot2grid((2,3),(0,0)) # 在一张大图里分列几个小图\n",
    "plt.subplots(2,3,figsize=(15,10))\n",
    "data_train.Survived.value_counts().plot(kind='bar')# 柱状图\n",
    "plt.title(u\"survived\") # 标题\n",
    "plt.ylabel(u\"counts\") # Y轴标签\n",
    "\n",
    "plt.subplot2grid((2,3),(0,1))\n",
    "data_train.Pclass.value_counts().plot(kind=\"bar\") # 柱状图显示\n",
    "plt.ylabel(u\"counts\")\n",
    "plt.title(u\"Pclass\")\n",
    "\n",
    "plt.subplot2grid((2,3),(0,2))\n",
    "plt.scatter(data_train.Survived, data_train.Age) #为散点图传入数据\n",
    "plt.ylabel(\"年龄\") # 设定纵坐标名称\n",
    "plt.grid(b=True, which='major', axis='y')\n",
    "plt.title(u\"Age for survived\")\n",
    "\n",
    "plt.subplot2grid((2,3),(1,0), colspan=2)\n",
    "data_train.Age[data_train.Pclass == 1].plot(kind='kde') # 密度图\n",
    "data_train.Age[data_train.Pclass == 2].plot(kind='kde')\n",
    "data_train.Age[data_train.Pclass == 3].plot(kind='kde')\n",
    "plt.xlabel(u\"age\")# plots an axis lable\n",
    "plt.ylabel(u\"density\")\n",
    "plt.title(u\"density fro age in Pclass\")\n",
    "plt.legend((u'P1', u'P2',u'P3'),loc='best') # sets our legend for our graph.\n",
    "\n",
    "plt.subplot2grid((2,3),(1,2))\n",
    "data_train.Embarked.value_counts().plot(kind='bar')\n",
    "plt.title(u\"Embarked\")\n",
    "plt.ylabel(u\"counts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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